Last updated: 2019-08-19

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#Translation from HTSeq raw counts -> Count Matrix I have 68 TCGA patients with whole-genome sequencing data and RNAseq data as well as 50 Nigerian patients with RNA-seq data. Raw counts were initially processed using HTSeq, so HTSeq data is being formatted for use with DESeq2 and limma-voom.

FOLDER <- "/Users/parajago/Research-Local/RNAseq-Local/Inputs/NigerianTCGA_quants-proteincoding-IHC"
sampleFiles <- grep("htseq.counts",list.files(FOLDER),value=TRUE)

#Differential gene expression setup based on race (b/w/other)
sampleConditionrace <- sampleFiles
countVar2=1
for (sample in sampleConditionrace){
  if (stri_detect_fixed(sample,"LIB")==TRUE){
    sampleConditionrace[countVar2] <- "Nigerian"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"black")==TRUE){
    sampleConditionrace[countVar2] <- "TCGA_black"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"white")==TRUE){
    sampleConditionrace[countVar2] <- "TCGA_white"
    countVar2=countVar2+1
  } else{
    sampleConditionrace[countVar2] <- "TCGA_other"
    countVar2=countVar2+1
  }
}

#Condition based on IHC subtype 
sampleConditionIHC <- sampleFiles
countVar2=1
for (sample in sampleConditionIHC){
  if (stri_detect_fixed(sample,"Her2")==TRUE){
    sampleConditionIHC[countVar2] <- "Her2"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"TNBC")==TRUE){
    sampleConditionIHC[countVar2] <- "TNBC"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"HRpos")==TRUE){
    sampleConditionIHC[countVar2] <- "HRpos"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"TPBC")==TRUE){
    sampleConditionIHC[countVar2] <- "TPBC"
    countVar2=countVar2+1
  } else{
    sampleConditionIHC[countVar2] <- "Other"
    countVar2=countVar2+1
  }
}

#Condition based on batch (relevant to the Nigerian patients only; no difference in batch for the TCGA patients)
batchval <- sampleFiles
countVar2=1
for (sample in batchval){
  if (stri_detect_fixed(sample,"batch1")==TRUE){
    batchval[countVar2] <- "batch1"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"batch23")==TRUE){
    batchval[countVar2] <- "batch23"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"batch4")==TRUE){
    batchval[countVar2] <- "batch4"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"batch5")==TRUE){
    batchval[countVar2] <- "batch5"
    countVar2=countVar2+1
  } else{
    batchval[countVar2] <- "batchT"
    countVar2=countVar2+1
  }
}

sampleLOH <-sampleFiles
countVar2=1
for (sample in sampleLOH){
  if (stri_detect_fixed(sample,"noLOH")==TRUE){
    sampleLOH[countVar2] <- "noLOH"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"LOH")==TRUE){
    sampleLOH[countVar2] <- "LOH"
    countVar2=countVar2+1
  } else{
    sampleLOH[countVar2] <- NA
    countVar2=countVar2+1
  }
}

table(sampleConditionrace, sampleConditionIHC)
                   sampleConditionIHC
sampleConditionrace Her2 HRpos TNBC
         Nigerian     23     7   20
         TCGA_black    0    12   20
         TCGA_white   10    10   16
sampleTable <- data.frame(sampleName=gsub(".htseq.counts","",sampleFiles),
                          fileName=sampleFiles,
                          condition1=sampleConditionrace,
                          condition2=sampleConditionIHC,
                          condition3=sampleLOH,
                          batch=batchval)

sampleTable$sampleCondition <- paste(sampleTable$condition1, sampleTable$condition2, sep=".")

ddsHTSeqMF <- DESeqDataSetFromHTSeqCount(sampleTable=sampleTable,
                                       directory=FOLDER,
                                       design=~sampleCondition)

ddsHTSeqMF <- ddsHTSeqMF[rowSums(counts(ddsHTSeqMF)) > 0, ] #Pre-filtering the dataset by removing the rows without any  information about gene expression -> this removes 675 genes

#Quantile normalization Please refer to: https://parajago.github.io/TCGA-Nigerian-RNAseq/NigerianTCGArawcountsDeSeq2-pc2.html regarding comparison between the Nigerian and TCGA data sets and why quantile normalization under the limma-voom approach was chosen for primary differential expression analysis.

##Data visualization

countmatrix <- assay(ddsHTSeqMF) #Raw counts organized into matrix format from individual files
countmatrix2 <- log2(countmatrix + 1) #Basic transformation of the count data 

plot(density(countmatrix2[,1]),lwd=3,ylim=c(0,.30), main="Density of counts with log2[count]+1 transformation ONLY") 
for(i in 1:118){lines(density(countmatrix2[,i]),lwd=3)} #This demonstrates that there is a difference in distributions between the Nigerian and TCGA data with basic log transformation normalization 

norm_countmatrix <- as.matrix(countmatrix2) 
norm_countmatrix = normalize.quantiles(norm_countmatrix)
plot(density(norm_countmatrix[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization")
for(i in 1:118){lines(density(norm_countmatrix[,i]),lwd=3)} #This demonstrates the effect of comparative quantile normalization

colnames (norm_countmatrix) <- colnames (countmatrix2)
rownames (norm_countmatrix) <- rownames (countmatrix2)

norm_countmatrix <- as.data.frame(norm_countmatrix)
countmatrixNigerian <- dplyr::select(norm_countmatrix, contains("LIB"))
plot(density(countmatrixNigerian[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization - Nigerian")
for(i in 1:50){lines(density(countmatrixNigerian[,i]),lwd=3)} #This demonstrates the result of the normalized Nigerian counts separately

tcgacolnames <- colnames(countmatrix)
tcgacolnames <- setdiff(tcgacolnames, colnames(countmatrixNigerian))
countmatrixTCGA <- norm_countmatrix[ , tcgacolnames]
plot(density(countmatrixTCGA[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization - TCGA")
for(i in 1:68){lines(density(countmatrixTCGA[,i]),lwd=3)} #This demonstrates the result of the normalized TCGA counts separately

norm_countmatrix <- as.data.frame(norm_countmatrix)
t_norm_countmatrix <- t(norm_countmatrix)

t_norm_countmatrix <- cbind (t_norm_countmatrix, sampleTable) #This binds the characteristics of the original patients to the quantile normalized counts. CBinding was checked to make sure that patients were correctly aligned to characteristics. 

quant.pca <- prcomp(t_norm_countmatrix[,1:19652])
autoplot(quant.pca, data=t_norm_countmatrix, colour='sampleCondition', main="PCA of quantile normalization results prior to DE analysis")

In the raw data with log transformation only, we are able to see that there are two peaks corresponding to the two datasets (Nigerian and TCGA). The quantile normalization demonstrates a PCA that has similar clustering. Only ~20% of the distribution of the data set is explained by the PCA1, 2 of the variables.

##Differential expression setup

annotation <- as.data.frame(row.names(countmatrix))
colnames(annotation) <- c("GeneID")
annotation$temp <- gsub("[.].+", "", annotation[,1])

annotation$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

annotation$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

annotation$chr <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="SEQNAME",
                     keytype="GENEID",           
                     multiVals="first")

annotation$locstart <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="GENESEQSTART",
                     keytype="GENEID",
                     multiVals="first")

annotation$locend <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="GENESEQEND",
                     keytype="GENEID",
                     multiVals="first")
annotation$temp <- NULL

design <- t_norm_countmatrix
design <- design %>% dplyr::select(sampleCondition)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian vs. TCGA regardless of subtype

designNvsT <- t_norm_countmatrix
designNvsT <- designNvsT %>% dplyr::select(condition1)

designNvsT$condition1 <- ifelse (designNvsT$condition1=="TCGA_white", 0, as.character(designNvsT$condition1))

designNvsT$condition1 <- ifelse (designNvsT$condition1=="TCGA_black", 1, as.character(designNvsT$condition1))

designNvsT$condition1 <- ifelse (designNvsT$condition1=="Nigerian", 2, as.character(designNvsT$condition1))

designNvsT$TCGA_white <- ifelse (designNvsT$condition1==0, 1, 0)

designNvsT$TCGA_black <- ifelse (designNvsT$condition1==1, 1, 0)

designNvsT$Nigerian <- ifelse (designNvsT$condition1==2, 1, 0)

designNvsT$condition1 <- NULL

mm <- model.matrix(~0+designNvsT$TCGA_white+designNvsT$TCGA_black+designNvsT$Nigerian)

quantids <- rownames(designNvsT)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "TCGA_black", "Nigerian")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)
dim(d0)
[1] 19652   118
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 16711   118
v=voom(d,designNvsT,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(TCGA_white-Nigerian,levels=colnames(designNvsT))

fit <- lmFit(v, designNvsT)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit, trend=TRUE)
dt <- decideTests(fit)
summary(dt)
       TCGA_white - Nigerian
Down                    3440
NotSig                  9111
Up                      4160
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression between breast cancers in\n Nigerian vs. TCGA white breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian and TCGA white patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAwhite-Nigerian-all-DE.csv", row.names = FALSE)


contr.matrix <- makeContrasts(TCGA_black-Nigerian,levels=colnames(designNvsT))

fit <- lmFit(v, designNvsT)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit, trend=TRUE)
dt <- decideTests(fit)
summary(dt)
       TCGA_black - Nigerian
Down                    3132
NotSig                  9587
Up                      3992
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression between breast cancers in\n Nigerian vs. TCGA black breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian and TCGA black patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAblack-Nigerian-all-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - TNBC

designNvsW <- design
designNvsW$sampleCondition <- ifelse (designNvsW$sampleCondition=="TCGA_white.TNBC", 0, as.character(designNvsW$sampleCondition))
designNvsW$sampleCondition <- ifelse (designNvsW$sampleCondition=="Nigerian.TNBC", 1, as.character(designNvsW$sampleCondition))

designNvsW$sampleCondition <- ifelse (designNvsW$sampleCondition==0 | designNvsW$sampleCondition==1, designNvsW$sampleCondition, NA)

designNvsW <- designNvsW %>% subset(is.na(sampleCondition)==FALSE)

designNvsW$TCGA_white.TNBC <- ifelse (designNvsW$sampleCondition==0, 1, 0)
designNvsW$Nigerian.TNBC <- ifelse (designNvsW$sampleCondition==1, 1, 0)

designNvsW$sampleCondition <- NULL

mm <- model.matrix(~0+designNvsW$TCGA_white.TNBC+designNvsW$Nigerian.TNBC)

quantids <- rownames(designNvsW)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "Nigerian")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 14543    36
v=voom(d,designNvsW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(TCGA_white.TNBC-Nigerian.TNBC, levels=colnames(designNvsW))

fit <- lmFit(v, designNvsW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       TCGA_white.TNBC - Nigerian.TNBC
Down                              1998
NotSig                            9679
Up                                2866
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between TNBC breast cancers in Nigerian and TCGA white patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between TNBC \nbreast cancers in Nigerian and TCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAwhite-TNBC.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - TNBC

designNvsB <- design
designNvsB$sampleCondition <- ifelse (designNvsB$sampleCondition=="TCGA_black.TNBC", 0, as.character(designNvsB$sampleCondition))
designNvsB$sampleCondition <- ifelse (designNvsB$sampleCondition=="Nigerian.TNBC", 1, as.character(designNvsB$sampleCondition))

designNvsB$sampleCondition <- ifelse (designNvsB$sampleCondition==0 | designNvsB$sampleCondition==1, designNvsB$sampleCondition, NA)

designNvsB <- designNvsB %>% subset(is.na(sampleCondition)==FALSE)

designNvsB$TCGA_black.TNBC <- ifelse (designNvsB$sampleCondition==0, 1, 0)
designNvsB$Nigerian.TNBC <- ifelse (designNvsB$sampleCondition==1, 1, 0)

designNvsB$sampleCondition <- NULL

mm <- model.matrix(~0+designNvsB$TCGA_black.TNBC+designNvsB$Nigerian.TNBC)

quantids <- rownames(designNvsB)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_black", "Nigerian")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 14642    40
v=voom(d,designNvsB,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(TCGA_black.TNBC-Nigerian.TNBC, levels=colnames(designNvsB))

fit <- lmFit(v, designNvsB)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       TCGA_black.TNBC - Nigerian.TNBC
Down                              2125
NotSig                            9880
Up                                2637
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between TNBC breast cancers in Nigerian and TCGA black patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between TNBC \nbreast cancers in Nigerian and TCGA black breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAblack-TNBC.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - HR+2-

designNvsWHR <- design
designNvsWHR$sampleCondition <- ifelse (designNvsWHR$sampleCondition=="TCGA_white.HRpos", 0, as.character(designNvsWHR$sampleCondition))
designNvsWHR$sampleCondition <- ifelse (designNvsWHR$sampleCondition=="Nigerian.HRpos", 1, as.character(designNvsWHR$sampleCondition))

designNvsWHR$sampleCondition <- ifelse (designNvsWHR$sampleCondition==0 | designNvsWHR$sampleCondition==1, designNvsWHR$sampleCondition, NA)

designNvsWHR <- designNvsWHR %>% subset(is.na(sampleCondition)==FALSE)

designNvsWHR$TCGA_white.HRpos <- ifelse (designNvsWHR$sampleCondition==0, 1, 0)
designNvsWHR$Nigerian.HRpos <- ifelse (designNvsWHR$sampleCondition==1, 1, 0)

designNvsWHR$sampleCondition <- NULL

mm <- model.matrix(~0+designNvsWHR$TCGA_white.HRpos+designNvsWHR$Nigerian.HRpos)

quantids <- rownames(designNvsWHR)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "Nigerian")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 13250    17
v=voom(d,designNvsWHR,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(TCGA_white.HRpos-Nigerian.HRpos, levels=colnames(designNvsWHR))

fit <- lmFit(v, designNvsWHR)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       TCGA_white.HRpos - Nigerian.HRpos
Down                                 579
NotSig                             12031
Up                                   640
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between HR-positive breast cancers in Nigerian and TCGA white patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between HR-postive \nbreast cancers in Nigerian and TCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGA_white-Nigerian-HRpos.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - HR+2-

designNvsBHR <- design
designNvsBHR$sampleCondition <- ifelse (designNvsBHR$sampleCondition=="TCGA_black.HRpos", 0, as.character(designNvsBHR$sampleCondition))
designNvsBHR$sampleCondition <- ifelse (designNvsBHR$sampleCondition=="Nigerian.HRpos", 1, as.character(designNvsBHR$sampleCondition))

designNvsBHR$sampleCondition <- ifelse (designNvsBHR$sampleCondition==0 | designNvsBHR$sampleCondition==1, designNvsBHR$sampleCondition, NA)

designNvsBHR <- designNvsBHR %>% subset(is.na(sampleCondition)==FALSE)

designNvsBHR$TCGA_black.HRpos <- ifelse (designNvsBHR$sampleCondition==0, 1, 0)
designNvsBHR$Nigerian.HRpos <- ifelse (designNvsBHR$sampleCondition==1, 1, 0)

designNvsBHR$sampleCondition <- NULL

mm <- model.matrix(~0+designNvsBHR$TCGA_black.HRpos+designNvsBHR$Nigerian.HRpos)

quantids <- rownames(designNvsBHR)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_black", "Nigerian")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 15862    19
v=voom(d,designNvsBHR,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(TCGA_black.HRpos-Nigerian.HRpos, levels=colnames(designNvsBHR))

fit <- lmFit(v, designNvsBHR)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       TCGA_black.HRpos - Nigerian.HRpos
Down                                 196
NotSig                             15625
Up                                    41
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between HR-positive breast cancers in Nigerian and TCGA black patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between HR-positive \nbreast cancers in Nigerian and TCGA black breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAblack-Nigerian-HRpos.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - HER2 (no TCGA Black HER2+ patients)

designNvsWHER2 <- design
designNvsWHER2$sampleCondition <- ifelse (designNvsWHER2$sampleCondition=="TCGA_white.Her2", 0, as.character(designNvsWHER2$sampleCondition))
designNvsWHER2$sampleCondition <- ifelse (designNvsWHER2$sampleCondition=="Nigerian.Her2", 1, as.character(designNvsWHER2$sampleCondition))

designNvsWHER2$sampleCondition <- ifelse (designNvsWHER2$sampleCondition==0 | designNvsWHER2$sampleCondition==1, designNvsWHER2$sampleCondition, NA)

designNvsWHER2 <- designNvsWHER2 %>% subset(is.na(sampleCondition)==FALSE)

designNvsWHER2$TCGA_white.Her2 <- ifelse (designNvsWHER2$sampleCondition==0, 1, 0)
designNvsWHER2$Nigerian.Her2 <- ifelse (designNvsWHER2$sampleCondition==1, 1, 0)

designNvsWHER2$sampleCondition <- NULL

mm <- model.matrix(~0+designNvsWHER2$TCGA_white.Her2+designNvsWHER2$Nigerian.Her2)

quantids <- rownames(designNvsWHER2)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "Nigerian")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 14148    33
v=voom(d,designNvsWHER2,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(TCGA_white.Her2-Nigerian.Her2, levels=colnames(designNvsWHER2))

fit <- lmFit(v, designNvsWHER2)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       TCGA_white.Her2 - Nigerian.Her2
Down                              1147
NotSig                           11167
Up                                1834
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between Her2+ breast cancers in Nigerian and TCGA white patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between Her2+ \nbreast cancers in Nigerian and TCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAwhite-Nigerian-Her2.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA - HER2 14q LOH

design <- t_norm_countmatrix
designNvsTLOH <- design %>% dplyr::select(condition3)

designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3=="noLOH", 0, as.character(designNvsTLOH$condition3))
designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3=="LOH", 1, as.character(designNvsTLOH$condition3))

designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3==0 | designNvsTLOH$condition3==1, designNvsTLOH$condition3, NA)

designNvsTLOH <- designNvsTLOH %>% subset(is.na(condition3)==FALSE)

designNvsTLOH$noLOH <- ifelse (designNvsTLOH$condition3==0, 1, 0)
designNvsTLOH$LOH <- ifelse (designNvsTLOH$condition3==1, 1, 0)

designNvsTLOH$condition3 <- NULL

mm <- model.matrix(~0+designNvsTLOH$noLOH+designNvsTLOH$LOH)

quantids <- rownames(designNvsTLOH)
rownames(mm) <- quantids
colnames(mm) <- c("noLOH", "LOH")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 13844    21
v=voom(d,designNvsTLOH,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(noLOH-LOH, levels=colnames(designNvsTLOH))

fit <- lmFit(v, designNvsTLOH)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       noLOH - LOH
Down             0
NotSig       13844
Up               0

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

No significant differential expression was identified via quantile normalization/voom, which is expected as this method can be overly convservative for inter-group differential expression estimation. We have previously validated DESeq2 for inter-Nigerian comparison.

sampleTable2 <- data.frame(sampleName=gsub(".htseq.counts","",sampleFiles),
                          fileName=sampleFiles,
                          sampleCondition=sampleLOH)

sampleTable2 <- sampleTable2 %>% subset(is.na(sampleCondition)==FALSE)

ddsHTSeqMF2 <- DESeqDataSetFromHTSeqCount(
                                      sampleTable=sampleTable2,
                                      directory=FOLDER,
                                      design=~sampleCondition)

ddsHTSeqMF2 <- ddsHTSeqMF2[rowSums(counts(ddsHTSeqMF2)) > 0, ] #Pre-filtering the dataset by removing the rows without any  information about gene expression

dds <- estimateSizeFactors(ddsHTSeqMF2) #The size factor is the median ratio of the sample over a "pseudosample": for each gene, the geometric mean of all samples. This accounts for sequencing depth. 

vsd <- vst(ddsHTSeqMF2, blind=FALSE) #Variance-stabilizing transformation, only using this since >50 samples

plotPCA(vsd, intgroup=c("sampleCondition"))

ddsMF <- DESeq(ddsHTSeqMF2)
resultsNames(ddsMF)
[1] "Intercept"                    "sampleCondition_noLOH_vs_LOH"
fc = 0.58 #Subsequent threshold of signifcance for log2 fold change -> 0.58 = log2(1.5)
fdr = 0.05 #Subsequent threshold of significance for p-value (adjusted by FDR)

cat("MA Plot: Differential expression based on 14q LOH presence")
MA Plot: Differential expression based on 14q LOH presence
res <- lfcShrink(ddsMF, coef="sampleCondition_noLOH_vs_LOH", type="ashr", optmethod = "mixEM")
DESeq2::plotMA(res, ylim=c(-10,10), xlim=c(0.1,200))

diffLOH<- results(ddsMF, contrast=c("sampleCondition", "noLOH", "LOH"), alpha=0.05)
nrow(diffLOH)
[1] 19071
diffLOH <- diffLOH[(diffLOH$log2FoldChange >= fc | diffLOH$log2FoldChange <= -fc),] 
diffLOH <- subset(diffLOH, padj < fdr)
nrow(diffLOH)
[1] 101
restemp <- lfcShrink(ddsMF, contrast=c("sampleCondition", "noLOH", "LOH"), res = diffLOH, type="ashr", optmethod = "mixEM")

restemp$temp <- row.names(restemp)
restemp$temp <- gsub("[.].+", "", restemp$temp)

restemp$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=restemp$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

with(restemp, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian patients with and without 14q LOH", xlim=c(-60,60), ylim=c(0,60)))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=symbol, cex=.5))

diffLOHtable <- as.data.frame(diffLOH)
diffLOHtable$temp <- row.names(diffLOH)
diffLOHtable$temp <- gsub("[.].+", "", diffLOHtable$temp)

diffLOHtable$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffLOHtable$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

diffLOHtable$biotype <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffLOHtable$temp,
                     column="GENEBIOTYPE",
                     keytype="GENEID",           
                     multiVals="first")

diffLOHtable$chr <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffLOHtable$temp,
                     column="SEQNAME",
                     keytype="GENEID",           
                     multiVals="first")

diffLOHtable$locstart <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffLOHtable$temp,
                     column="GENESEQSTART",
                     keytype="GENEID",
                     multiVals="first")

diffLOHtable$locend <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffLOHtable$temp,
                     column="GENESEQEND",
                     keytype="GENEID",
                     multiVals="first")

diffLOHtable$temp <- NULL

diffLOHtable <- diffLOHtable %>% arrange(log2FoldChange)
top_n(diffLOHtable, 10, log2FoldChange)
top_n(diffLOHtable, -10, log2FoldChange)
write.csv(diffLOHtable, file = "DE-LOH.csv")

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details. 


sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] Glimma_1.12.0               RColorBrewer_1.1-2         
 [3] preprocessCore_1.46.0       ashr_2.2-32                
 [5] ggfortify_0.4.7             calibrate_1.7.2            
 [7] MASS_7.3-51.4               sva_3.32.1                 
 [9] mgcv_1.8-28                 nlme_3.1-140               
[11] EnsDb.Hsapiens.v75_2.99.0   ensembldb_2.8.0            
[13] AnnotationFilter_1.8.0      GenomicFeatures_1.36.1     
[15] hexbin_1.27.3               stringi_1.4.3              
[17] dplyr_0.8.1                 affy_1.62.0                
[19] checkmate_1.9.3             pathview_1.24.0            
[21] org.Hs.eg.db_3.8.2          AnnotationDbi_1.46.0       
[23] clusterProfiler_3.12.0      pheatmap_1.0.12            
[25] genefilter_1.66.0           vsn_3.52.0                 
[27] RUVSeq_1.18.0               EDASeq_2.18.0              
[29] ShortRead_1.42.0            GenomicAlignments_1.20.0   
[31] Rsamtools_2.0.0             Biostrings_2.52.0          
[33] XVector_0.24.0              DESeq2_1.24.0              
[35] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[37] BiocParallel_1.18.0         matrixStats_0.54.0         
[39] Biobase_2.44.0              GenomicRanges_1.36.0       
[41] GenomeInfoDb_1.20.0         IRanges_2.18.1             
[43] S4Vectors_0.22.0            BiocGenerics_0.30.0        
[45] edgeR_3.26.4                limma_3.40.2               
[47] ggbiplot_0.55               scales_1.0.0               
[49] plyr_1.8.4                  ggplot2_3.1.1              
[51] gplots_3.0.1.1             

loaded via a namespace (and not attached):
  [1] R.utils_2.8.0          tidyselect_0.2.5       RSQLite_2.1.1         
  [4] htmlwidgets_1.3        DESeq_1.36.0           munsell_0.5.0         
  [7] codetools_0.2-16       withr_2.1.2            colorspace_1.4-1      
 [10] GOSemSim_2.10.0        knitr_1.23             rstudioapi_0.10       
 [13] pscl_1.5.2             DOSE_3.10.1            labeling_0.3          
 [16] git2r_0.25.2           KEGGgraph_1.44.0       urltools_1.7.3        
 [19] GenomeInfoDbData_1.2.1 mixsqp_0.1-97          hwriter_1.3.2         
 [22] polyclip_1.10-0        bit64_0.9-7            farver_1.1.0          
 [25] rprojroot_1.3-2        xfun_0.7               doParallel_1.0.14     
 [28] R6_2.4.0               locfit_1.5-9.1         bitops_1.0-6          
 [31] fgsea_1.10.0           gridGraphics_0.4-1     assertthat_0.2.1      
 [34] ggraph_1.0.2           nnet_7.3-12            enrichplot_1.4.0      
 [37] gtable_0.3.0           workflowr_1.4.0        rlang_0.3.4           
 [40] splines_3.6.0          rtracklayer_1.44.0     lazyeval_0.2.2        
 [43] acepack_1.4.1          europepmc_0.3          BiocManager_1.30.4    
 [46] yaml_2.2.0             reshape2_1.4.3         backports_1.1.4       
 [49] qvalue_2.16.0          Hmisc_4.2-0            tools_3.6.0           
 [52] ggplotify_0.0.3        affyio_1.54.0          ggridges_0.5.1        
 [55] Rcpp_1.0.1             base64enc_0.1-3        progress_1.2.2        
 [58] zlibbioc_1.30.0        purrr_0.3.2            RCurl_1.95-4.12       
 [61] prettyunits_1.0.2      rpart_4.1-15           viridis_0.5.1         
 [64] cowplot_0.9.4          ggrepel_0.8.1          cluster_2.0.9         
 [67] fs_1.3.1               magrittr_1.5           data.table_1.12.2     
 [70] DO.db_2.9              triebeard_0.3.0        truncnorm_1.0-8       
 [73] SQUAREM_2017.10-1      ProtGenerics_1.16.0    aroma.light_3.14.0    
 [76] hms_0.4.2              evaluate_0.14          xtable_1.8-4          
 [79] XML_3.98-1.20          gridExtra_2.3          compiler_3.6.0        
 [82] biomaRt_2.40.0         tibble_2.1.3           KernSmooth_2.23-15    
 [85] crayon_1.3.4           R.oo_1.22.0            htmltools_0.3.6       
 [88] Formula_1.2-3          tidyr_0.8.3            geneplotter_1.62.0    
 [91] DBI_1.0.0              tweenr_1.0.1           Matrix_1.2-17         
 [94] R.methodsS3_1.7.1      gdata_2.18.0           igraph_1.2.4.1        
 [97] pkgconfig_2.0.2        rvcheck_0.1.3          foreign_0.8-71        
[100] foreach_1.4.4          xml2_1.2.0             annotate_1.62.0       
[103] stringr_1.4.0          digest_0.6.19          graph_1.62.0          
[106] rmarkdown_1.13         fastmatch_1.1-0        htmlTable_1.13.1      
[109] curl_3.3               gtools_3.8.1           jsonlite_1.6          
[112] viridisLite_0.3.0      pillar_1.4.1           lattice_0.20-38       
[115] KEGGREST_1.24.0        httr_1.4.0             survival_2.44-1.1     
[118] GO.db_3.8.2            glue_1.3.1             UpSetR_1.4.0          
[121] iterators_1.0.10       png_0.1-7              bit_1.1-14            
[124] Rgraphviz_2.28.0       ggforce_0.2.2          blob_1.1.1            
[127] latticeExtra_0.6-28    caTools_1.17.1.2       memoise_1.1.0